Effective Traffic Flow Forecasting Using Taxi and Weather Data

Xu, Xiujuan; Su, Benzhe; Zhao, Xiaowei; Xu, Zhenzhen; Sheng, Quan Z.

doi:10.1007/978-3-319-49586-6_35

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Tsirigotis et al [33] considered only rainfall, along with traffic volume and speed to forecast the traffic. Similarly, Xu et al [34] considered temperature and humidity, along with taxi trajectory data to forecast traffic flow. They took travel time, pick-up & drop time, and distance into account to forecast traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

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Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.

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Section: Related Workmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, we can use Global Positioning System (GPS) data from taxis to measure traffic congestion based on speed [32]. Maghrebi et al [33] extracted tweets through Twitter Stream to analyze how people travel in their daily lives and found that walking and driving were the most frequent travel modes.…”

Section: Social Transportation In Itssmentioning

confidence: 99%

Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

et al. 2018

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Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.

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“…The bearing vibration signal data samples of coal mine main fans cover a large number of sample points, mostly 1000-4000 data points. Deep learning algorithms have defects in processing such data, and it is often difficult to achieve accurate diagnosis due to large computational complexity and imperfect feature learning system [21]. Therefore, in order for the fault diagnosis of the main fan bearing of the coal mine to be completed, it is necessary to find a way to improve the deep forest algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of Rolling Bearing Based on Wavelet Packet Analysis and Deep Forest

Zhang

Wang

et al. 2022

Symmetry

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The frequent accidents caused by the main fan motor in coal mines have exposed the safety hazards of rolling bearings. When a rolling bearing fails, its symmetry is broken, resulting in a rapid decline in its safety performance and posing a great threat to the main fan. Therefore, accurate rolling bearing fault diagnoses are the key to ensuring the safe and durable operation of main fans. Thus, in this paper, we propose a new fault diagnosis method of rolling bearing based on wavelet packet analysis and deep forest algorithm. Firstly, experiments were conducted under different health states to guarantee the diversity of data relating to the rolling bearing’s main fan and then to ensure the accuracy of the fault diagnosis under different health states. On the basis of the collected vibration signal data, we conducted the wavelet packet analysis method to extract the characteristics of the vibration signal and obtained a feature vector that characterizes the health of the bearing. After that, the extracted feature vector was used as the feature vector of the deep forest algorithm to train the deep forest diagnosis model and determine the location and fault type of the bearing fault. Finally, the proposed method in this paper was validated with real-time monitoring data of a main ventilation fan and compared with other diagnostic algorithms, which not only verified the diagnostic capability of deep forest in handling small samples, but also verified the diagnostic capability of the fault diagnosis model. In summary, the proposed fault diagnosis approach is promising in real coal mine main fans.

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Effective Traffic Flow Forecasting Using Taxi and Weather Data

Cited by 10 publications

References 13 publications

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

A Fault Diagnosis Method of Rolling Bearing Based on Wavelet Packet Analysis and Deep Forest

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